Exploring the Abnormal Brain Regions and Abnormal Functional Connections in SZ by Multiple Hypothesis Testing Technique

Schizophrenia(SZ)is one of the most common mental diseases.Its main characteristics are abnormal social behavior and inability to correctly understand real things.In recent years,the magnetic resonance imaging(MRI)technique has been popularly utilized to study SZ.However,it is still a great challenge to reveal the essential information contained in the MRI data.In this paper,we proposed a biomarker selection approach based on the multiple hypothesis testing techniques to explore the difference between SZ and healthy controls by using both functional and structural MRI data,in which biomarkers represent both abnormal brain functional connectivity and abnormal brain regions.By implementing the biomarker selection approach,six abnormal brain regions and twenty-three abnormal functional connectivity in the brains of SZ are explored.It is discovered that compared with healthy controls,the significantly reduced gray matter volumes are mainly distributed in the limbic lobe and the basal ganglia,and the significantly increased gray matter volumes are distributed in the frontal gyrus.Meanwhile,it is revealed that the significantly strengthened connections are those between the middle frontal gyrus and the superior occipital gyrus,the superior occipital gyrus and the middle occipital gyrus as well as the middle occipital gyrus and the fusiform gyrus,and the rest connections are significantly weakened.

A SAS Macro for Testing Differences among Three or More Independent Groups Using Kruskal-Wallis and Nemenyi Tests

As a nonparametric method,the Kruskal-Wallis test is widely used to compare three or more independent groups when an ordinal or interval level of data is available,especially when the assump-tions of analysis of variance (ANOVA) are not met.If the Kruskal-Wallis statistic is statistically signifi-cant,Nemenyi test is an alternative method for further pairwise multiple comparisons to locate the source of significance.Unfortunately,most popular statistical packages do not integrate the Nemenyi test,which is not easy to be calculated by hand.We described the theory and applications of the Kruskal-Wallis and Nemenyi tests,and presented a flexible SAS macro to implement the two tests.The SAS macro was demonstrated by two examples from our cohort study in occupational epidemiology.It provides a useful tool for SAS users to test the differences among three or more independent groups using a nonparametric method.

The time‑varying causal relationship between the Bitcoin market and internet attention

The increasing attention on Bitcoin since 2013 prompts the issue of possible evidence for a causal relationship between the Bitcoin market and internet attention.Taking the Google search volume index as the measure of internet attention,time-varying Granger causality between the global Bitcoin market and internet attention is examined.Empirical results show a strong Granger causal relationship between internet attention and trading volume.Moreover,they indicate,beginning in early 2018,an even stronger impact of trading volume on internet attention,which is consistent with the rapid increase in Bitcoin users following the 2017 Bitcoin bubble.Although Bitcoin returns are found to strongly affect internet attention,internet attention only occasionally affects Bitcoin returns.Further investigation reveals that interactions between internet attention and returns can be amplified by extreme changes in prices,and internet attention is more likely to lead to returns during Bitcoin bubbles.These empirical findings shed light on cryptocurrency investor attention theory and imply trading strategy in Bitcoin markets.

Multiple Choice Items in Testing

Language testing is very important and necessary,and moreover as we all know,nowadays,in English language testing, the muhiple-choice item is most widely used and many users regard the multiple-choice item as the most flexible and probably the most effective of the objective item types.The multiple-choice item has its characteristics,advantages and disadvantages.We should bring out its strengths to make up for its weaknesses and use it appropriately.Although it has its limitations,it is suitable for large-scale tests and tests dealing with wide-range knowledge.We should correctly ap- ply testing principles and methods in order to make testing more effective and reliable.

A computational model of the human glucose-insulin regulatory system

Objective：A computational model of insulin secretion and glucose metabolism for assisting the diagnosis of diabetes mellitus in clinical research is introduced.The proposed method for the estimation of parameters for a system of ordinary differential equations（ODEs）that represent the time course of plasma glucose and insulin concentrations during glucose tolerance test（GTT）in physiological studies is presented.The aim of this study was to explore how to interpret those laboratory glucose and insulin data as well as enhance the Ackerman mathematical model.Methods：Parameters estimation for a system of ODEs was performed by minimizing the sum of squared residuals（SSR）function,which quantifies the difference between theoretical model predictions and GTT＇s experimental observations.Our proposed perturbation search and multiple-shooting methods were applied during the estimating process.Results：Based on the Ackerman＇s published data,we estimated the key parameters by applying R-based iterative computer programs.As a result,the theoretically simulated curves perfectly matched the experimental data points.Our model showed that the estimated parameters,computed frequency and period values,were proven a good indicator of diabetes.Conclusion：The present paper introduces a computational algorithm to biomedical problems,particularly to endocrinology and metabolism fields,which involves two coupled differential equations with four parameters describing the glucose-insulin regulatory system that Ackerman proposed earlier.The enhanced approach may provide clinicians in endocrinology and metabolism field insight into the transition nature of human metabolic mechanism from normal to impaired glucose tolerance.

Correlation of Epworth Sleepiness Scale with multiple sleep latency test and its diagnostic accuracy in assessing excessive daytime sleepiness in patients with obstructive sleep apnea hypopnea syndrome

Background Excessive daytime sleepiness （EDS） is often associated with obstructive sleep apnea hypopnea syndrome （OSAHS） and contributes to a number of comorbidities in these patients. Therefore, early detection of EDS is critical in disease management. We examined the association between Epworth Sleepiness Scale （ESS） and multiple sleep latency test （MSLT） and diagnostic accuracy of ESS in assessing EDS in OSAHS patients. Methods The ESS, MSLT and overnight polysomnography were administered to 107 Chinese patients to assess EDS and its correlations with polysomnographic parameters. The diagnostic accuracy of ESS in classifying EDS （mean sleep latency （MSL） 〈10 minutes） was evaluated by calculating the area under ROC curve. Results As the severity of OSAHS increased, MSL decreased with increase in ESS score. Conversely, patients with worsening EDS （shorter MSL） were characterized by advanced nocturnal hypoxaemia and sleep disruption compared to those with normal MSL, suggesting EDS is associated with more severe OSAHS. There was a negative correlation between ESS score and MSL and both moderately correlated with some polysomnographic nocturnal hypoxaemic parameters. The area under ROC curve of ESS for identifying EDS was 0.80 （95% CI： 0.71 to 0.88） and ESS score 〉12 provided the best predictive value with a sensitivity of 80% and specificity of 69%. Conclusion The ESS score moderately correlates with MSL and our ROC study supports ESS as a screening strategy for assessing EDS in OSAHS.

Distributed Penalized Modal Regression for Massive Data

Nowadays,researchers are frequently confronted with challenges from massive data computing by a number of limitations of computer primary memory.Modal regression(MR)is a good alternative of the mean regression and likelihood based methods,because of its robustness and high efficiency.To this end,the authors extend MR to massive data analysis and propose a computationally and statistically efficient divide and conquer MR method(DC-MR).The major novelty of this method consists of splitting one entire dataset into several blocks,implementing the MR method on data in each block,and deriving final results through combining these regression results via a weighted average,which provides approximate estimates of regression results on the entire dataset.The proposed method significantly reduces the required amount of primary memory,and the resulting estimator is theoretically as efficient as the traditional MR on the entire data set.The authors also investigate a multiple hypothesis testing variable selection approach to select significant parametric components and prove the approach possessing the oracle property.In addition,the authors propose a practical modified modal expectation-maximization(MEM)algorithm for the proposed procedures.Numerical studies on simulated and real datasets are conducted to assess and showcase the practical and effective performance of our proposed methods.

maging genetics -towards discovery neuroscience

Imaging genetics is an emerging field aimed at identifying and characterizing genetic variants that influence measures derived from anatomical or functional brain images, which are in turn related to brain-related illnesses or fundamental cognitive, emotional and behavioral processes, and are affected by environmental factors. Here we review the recent evolution of statistical approaches and outstanding challenges in imaging genetics, with a focus on population-based imaging genetic association studies. We show the trend in imaging genetics from candidate approaches to pure discovery science, and from univariate to multivariate analyses. We also discuss future directions and prospects of imaging genetics for ultimately helping understand the genetic and environmental underpinnings of various neuropsychiatric disorders and turning basic science into clinical strategies.

FDR Control for Pairwise Comparisons of Normal Means Based on Goodness of Fit Test

This study is undertaken to apply a bootstrap method of controlling the false discovery rate （FDR） when performing pairwise comparisons of normal means. Due to the dependency of test statistics in pairwise comparisons, many conventional multiple testing procedures can＇t be employed directly. Some modified pro- cedures that control FDR with dependent test statistics are too conservative. In the paper, by bootstrap and goodness-of-fit methods, we produce independent p-values for pairwise comparisons. Based on these indepen- dent p-values, plenty of procedures can be used, and two typical FDR controlling procedures are applied here. An example is provided to illustrate the proposed approach. Extensive simulations show the satisfactory FDR control and power performance of our approach. In addition, the proposed approach can be easily extended to more than two normal, or non-normal, balance or unbalance cases.

Null-free False Discovery Rate Control Using Decoy Permutations

The traditional approaches to false discovery rate(FDR)control in multiple hypothesis testing are usually based on the null distribution of a test statistic.However,all types of null distributions,including the theoretical,permutation-based and empirical ones,have some inherent drawbacks.For example,the theoretical null might fail because of improper assumptions on the sample distribution.Here,we propose a null distributionfree approach to FDR control for multiple hypothesis testing in the case-control study.This approach,named target-decoy procedure,simply builds on the ordering of tests by some statistic or score,the null distribution of which is not required to be known.Competitive decoy tests are constructed from permutations of original samples and are used to estimate the false target discoveries.We prove that this approach controls the FDR when the score function is symmetric and the scores are independent between different tests.Simulation demonstrates that it is more stable and powerful than two popular traditional approaches,even in the existence of dependency.Evaluation is also made on two real datasets,including an arabidopsis genomics dataset and a COVID-19 proteomics dataset.

Variable Selection in High-Dimensional Error-in-Variables Models via Controlling the False Discovery Proportion

Multiple testing has gained much attention in high-dimensional statistical theory and applications,and the problem of variable selection can be regarded as a generalization of the multiple testing.It is aiming to select the important variables among many variables.Performing variable selection in high-dimensional linear models with measurement errors is challenging.Both the influence of high-dimensional parameters and measurement errors need to be considered to avoid severely biases.We consider the problem of variable selection in error-in-variables and introduce the DCoCoLasso-FDP procedure,a new variable selection method.By constructing the consistent estimator of false discovery proportion(FDP)and false discovery rate(FDR),our method can prioritize the important variables and control FDP and FDR at a specifical level in error-in-variables models.An extensive simulation study is conducted to compare DCoCoLasso-FDP procedure with existing methods in various settings,and numerical results are provided to present the efficiency of our method.

The influence of misspecified covariance on false discovery control when usingposterior probabilities

This paper focuses on the influence of a misspecified covariance structure on false discoveryrate for the large-scale multiple testing problem.Specifically,we evaluate the influence on themarginal distribution of local false discovery rate statistics,which are used in many multiple testing procedures and related to Bayesian posterior probabilities.Explicit forms of the marginaldistributions under both correctly specified and incorrectly specified models are derived.TheKullback–Leibler divergence is used to quantify the influence caused by a misspecification.Several numerical examples are provided to illustrate the influence.A real spatio-temporal data onsoil humidity is discussed.

A Polysomnography Study of Kleine-Levin Syndrome in a Single Center

Background： Kleine-Levin syndrome （KLS） is a rare sleep disorder characterized by recurrent episodes of hypersomnia. Polysomnographic （PSG） researches of KLS have been reported only in few publications in the past decades. This study aimed to investigate the characteristics of PSG of KLS. Methods： This study, which was conducted from March 2010 to July 2014, included seven patients diagnosed with KLS in the Sleep and Wake Disorder Center of Huashan Hospital, Fudan University （Shanghai, China）. PSG and multiple sleep latency tests （MSLT） were performed during their episodes and the results were evaluated. Results： Five of the seven patients were males, The mean age at KLS onset was 15.6 ：k 3.6 years. The number of episodes ranged from 2 to 7. The duration of episodes lasted from 4 to 11 days. The sleep architecture and proportion were normal in most of the patients. The average value of mean sleep latency was 6.9 4- 4.1 min. No sleep-onset rapid eye movement （SOREM） was detected in three of the patients, whereas one patient experienced one period of SOREM, and such episodes occurred twice in other two patients. Conclusions： We found that sleep architecture and proportion were normal in most KLS patients. However, the results of PSG and MSLT had no specificity for KLS patients.

Analytical study of friction coefficients of pomegranate seed as essential parameters in design of post-harvest equipment

Friction coefficients(static friction coefficient(SFC)and dynamic friction coefficient(DFC))of pomegranate seed on different structural surfaces(glass,aluminum,plywood,galvanized steel and rubber)as affected by moisture content(4-21.9%(d.b.))and sliding velocity(1.4-16(cm/s))were investigated.Analysis of variance(ANOVA)was performed to determine the effect of main treatments and their interactions on SFC and DFC.Significance of single or multiple effect of the main treatments with five levels was assessed using Duncan’s multiple range test(DMRT).To predict SFC and DFC,multiple linear regression(MLR)modeling technique was applied for each type of structural surface.The goodness of fit of each MLR model was evaluated using statistical parameters:coefficient of determination,root mean square error and mean relative deviation modulus.Results showed that the minimum and maximum SFC or DFC were in minimum and maximum moisture content on glass and rubber surface,respectively.ANOVA table indicated the significant effect of main treatments and their interactions on SFC and DFC at significance level of 1%(P<0.01).According to DMRT results,SFC linearly increased as moisture content increased and DFC increased also linearly as individual or simultaneous increment of moisture content and sliding velocity occurred,for all experimental conditions.According to the obtained statistical parameters,both SFC and DFC were properly predicted by means of MLR modeling technique.

A novel target detection approach based on adaptive radar waveform design

To resolve problems of complicated clutter, fast-varying scenes, and low signal-clutterratio （SCR） in application of target detection on sea for space-based radar （SBR）, a target detection approach based on adaptive waveform design is proposed in this paper. Firstly, complicated sea clutter is modeled as compound Gaussian process, and a target is modeled as some scatterers with Gaussian reflectivity. Secondly, every dwell duration of radar is divided into several sub-dwells. Regular linear frequency modulated pulses are transmitted at Sub-dwell 1, and the received signal at this sub-dwell is used to estimate clutter covariance matrices and pre-detection. Estimated matrices are updated at every following sub-dwell by multiple particle filtering to cope with fast-varying clutter scenes of SBR. Furthermore, waveform of every following sub-dwell is designed adaptively according to mean square optimization technique. Finally, principal component analysis and generalized likelihood ratio test is used for mitigation of colored interference and property of constant false alarm rate, respectively. Simulation results show that, considering configuration of SBR and condition of complicated clutter, 9 dB is reduced for SCR which reliable detection requires by this target detection approach. Therefore, the work in this paper can markedly improve radar detection performance for weak targets.

Optimum angle-cut of collimator for dense objects in high-energy proton radiography

The use of minus identity lenses with an angle-cut collimator can achieve high contrast images in highenergy proton radiography.This article presents the principles of choosing the angle-cut aperture of the collimator for different energies and objects.Numerical simulation using the Monte Carlo code Geant4 has been implemented to investigate the entire radiography for the French test object.The optimum angle-cut apertures of the collimators are also obtained for different energies.